Texture segmentation is a widely studied area of research in signal/image processing, which aims to divide a time-series or spatial dataset into sub-regions that have high internal homogeneity but differ significantly from each other. The seismic analysis community has also produced some methods that can segment seismic data automatically to facilitate faster analysis. However, most of these methods are based on designing “texture attributes”, or derived properties of the signal, i.e. the data, that can highlight different textures a priori. Other methods begin with small homogeneous regions that are then sequentially merged according to some pre-defined similarity criterion. Still other approaches use libraries of templates or training data for segmentation. Some examples of existing approaches are shown in the following list:    1. S. Todorovic and N. Ahuja, “Texel-based Texture Segmentation”, Proc. IEEE Int'l. Conf. Computer Vision (ICCV), Kyoto, Japan, 2009    2. M. Donoser and H. Bischof, “Using covariance matrices for unsupervised texture segmentation”, ICPR, 2008    3. M. Galun, E. Sharon, R. Basri and A. Brandt, “Texture segmentation by multiscale aggregation of filter responses and shape elements”, ICCV, pp. 716-723, 2003    4. R. M. Haralick, “Statistical and structural approaches to texture”, Proc. Of the IEEE 67(5), pp. 786-804 (1979)    5. J. H. Hays, M. Leordeanu, A. A. Efros and Y. Liu, “Discovering texture regularity as a higher-order correspondence problem”, ECCV 2, pp. 522-535, 2006    6. T. Hofmann, J. Puzicha, J. M. Buhmann and R. Friedrich, “Unsupervised texture segmentation in a deterministic annealing framework”, IEEE TPAMI 20, pp. 803-818, 1998    7. B. Julesz, “Textons, the elements of texture perception and their interactions”, Nature 290, pp. 91-97, 1981    8. T. Leung and J. Malik, “Representing and recognizing the visual appearance of materials using three-dimensional textons”, IJCV 43(2), pp. 29-44, 2001    9. W. C. Lin and Y. Liu, “A lattice-based MRF model for dynamic near-regular texture tracking”, IEEE Trans. Pattern Anal. Mach. Intell. 29(5), pp. 777-792, 2007    10. J. Malik, S. Belongie, T. Leung and J. Shi, “Contour and texture analysis for image segmentation”, Int. J. Comput. Vision 43(1), pp. 7-27, 2001    11. B. Schachter and N. Ahuja, “Random pattern generation processes”, CGIP 10(2), 95-114, 1979    12. M. Varma and A. Zisserman, “A statistical approach to texture classification from single images”, IJCV 62(1-2), pp. 61-81, 2005    13. H. Voorhees and T. Poggio, “Computing texture boundaries from images”, Nature 333, pp. 364-367, 1988    14. S. C. Zhu, C. E. Guo, Y. Wang and Z. Wu, “What are textons?”, IJCV 62(1-2), pp. 121-143, 2005    15. M. Fernandez Limia, A. Mavilio Nunez, M. Tejera Fernandez, “Texture Segmentation of a 3D Seismic Section with Wavelet Transform and Gabor Filters”, ICPR 3, pg. 3358, 15th Int'l. Conf. on Pattern Recognition, 2000    16. Brian West and Steve May, “Method for Seismic Facies Interpretation Using Textural Analysis and Neural Networks,” U.S. Pat. No. 6,560,540.
Other examples are compiled and discussed in the survey article by Jean-Pascal Aribot entitled Texture Segmentation that may be downloaded at http://www.mics.ch/SumIntU03/JPAribot.pdf.
The present invention is a method that does not require any training data, attributes or pre-defined similarity measures designed to distinguish textures. Instead, it extracts statistical distributions of user-defined windows on the data, and clusters the distributions according to a standard similarity metric between probability distributions, including similarity metrics commonly used in the literature. This lack of pre-defined criteria, combined with the sensitivity of the method, which enables significantly better and more robust results than alternatives, make the present inventive method particularly advantageous for datasets that do not have a well known pre-defined structure, which is often the case for complex datasets such as seismic data.